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Revitalizing the CS Curriculum
David Klappholz (Stevens)
Steven Condly (UCF)
Allen Johnson (HTU)
Ken Modesit (IPFW)
Vicki Almstrum (UT Austin)
Cherry Owen (UTPB)
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Intelligence and Expertise
Interactions with Implications for
Training, Education, and Transfer
Software Development
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Requirements Engineering
High-Level Design
Low-Level Design
Implementation
Testing
Deployment
Maintenance & Enhancement
Taught in CS Programs
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Requirements Engineering (sometimes)
High-Level Design (sometimes)
Low-Level Design (always)
Implementation (always)
Testing (just a bit, if at all)
Deployment (sometimes)
Maintenance & Enhancement (never?)
When/Where?
• Requirements Engineering (usually in an RPRCC, if
there is one. Almost never if there isn’t one)
• High-Level Design (SE course; OOA&D course; often
not required)
• Low-Level Design (core CS courses)
• Implementation (core CS courses)
• Testing (a tiny bit in CS1/CS2; SE course – often not
required; often in an RPRCC, if there is one)
• Deployment (usually in an RPRCC, if there is one.
Almost never if there isn’t one)
• Maintenance & Enhancement (never?)
Can Be Offshored?
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Requirements Engineering (*)
High-Level Design (**)
Low-Level Design (**********)
Implementation (**********)
Testing (*****)
Deployment (*)
Maintenance & Enhancement (?)
Gender of Those With Greatest Interest &
(Probably) Greatest Innate Ability, Especially
if Project is Socially Relevant?
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Requirements Engineering (F)
High-Level Design (M/F)
Low-Level Design (M)
Implementation (M)
Testing (M/F?)
Deployment (F?)
Maintenance & Enhancement (nobody?)
And Yet We:
• Worry about recruiting CS majors in the face of
offshoring
• Wonder why the software development failure
rate is as high as it is (Chaos Reports)
• Why almost no young women are entering CS
majors
• Spend millions of dollars on interventions to get
young women excited about programming
(Alice, TeachScheme, IBM and Microsoft
summer camps for girls)
Possible Solution:
RPRCC-Centric CS
Curricula
RPRCCs: Real Projects for Real
Clients Courses
• Senior-Level Capstone Course
• Sophomore/Junior-Level Web Programming Course
– Students love it
– No technical content need be sacrificed
• Sophomore/Junior-Level DBMS Course
– Students love it
– No technical content need be sacrificed
• Freshman-Level Web Site Design Course
– Programming need not be a pre-requisite
– No programming need be taught
– GUI design, some web-page development tool, and, possibly some HCI
are taught
– Students in a later course do the implementation, etc.
– Choose at least some of the projects for social relevance, to attract
young women
• High School: precisely the same as freshman-level college course
RPRCC-Centric Curriculum
• RPRCCs at all levels
• No reduction in technical courses or in
technical content of any existing courses –
including RPRCC-ified courses
• Benefits: next slide
Benefits
– Recruitment and retention of young women
& members of under-represented minorities
– All students learn more non-offshorable
skills
– Each important software development topic
and skill is taught informally in earlier
RPRCCs and then covered in more detail
or more formally in one or more later
RPRCCs (continued on next slide)
Benefits
• In early RPRCC teach relatively informal
requirements engineering and requirements
documentation techniques, with more formal
specification techniques taught in later
RPRCCs
• Teach simple cost and effort
estimation/scheduling in early courses…
• Introduce large-project skills in capstone
course or as part of a separate late-curriculum
SE course. Students learn skills needed for
large projects after they already understand the
need for small-project SE skills and are better
prepared intellectually to understand when and
why large-project skills are needed.
Benefits
• Introduce large-project skills in capstone course or as
part of a separate late-curriculum SE course. Students
learn skills needed for large projects after they already
understand the need for small-project SE skills and are
better prepared intellectually to understand when and
why large-project skills are needed.
• There are skills that can only be learned through
repeated use – e.g., choosing a development process
at a proper point between light-weight (or agile) and
heavier weight
• Repetitive, incremental approach to teaching RPRCC
skills help students better learn concepts that are easy
to explain but difficult to perform, such as risk
management
Benefits
Real Projects for Real Clients
Requires (Learning) Real Testing
Intelligence and Expertise
Interactions with Implications for
Training, Education, and Transfer
Intelligence
• Capacity for learning, reasoning,
understanding, and similar forms of
mental activity; aptitude in grasping truths,
relationships, facts, meanings, etc.
• The ability to comprehend; to understand
and profit from experience
• Represented by symbol g
Facts About Intelligence
• Most studied phenomenon in psychology
• Can be reliably measured with a highly gloaded test (e.g., Ravens PM and APM)
• Resultant “score” (IQ) single most valid
predictor of success in life (grades,
amount of schooling, income, job prestige,
likelihood of incarceration, resilience)
Facts About Intelligence
• Is essentially a genetic phenomenon,
though is affected by environment
• Attempts to raise collective IQ scores
substantially and persistently have failed
• Illegal to measure in schools (except for
special education / gifted enrollment)
• Mostly illegal to measure in the workplace
Expertise
• Skill or knowledge in a particular area
• Skillfulness by virtue of possessing special
knowledge
• Anyone can become an expert in any
domain if you practice smart and hard
enough
How Intelligence Influences
Expertise
• Has a predisposing effect on student
readiness
– High IQ students more likely to enter class better
prepared
• Affects the rate at which information is
acquired, processed, stored, retrieved, and
utilized
• Assists in adaptation / adjustment in
application of skills
• Contributes to originality / creativity
Transfer
• The application of knowledge learned in one
setting or for one purpose to another setting
or purpose.
• Initial transfer is explained in terms of overlap
of declarative components
• Sustained transfer, in terms of procedural
(skill) components
• The best way to get information about the
conceptual understanding required for
transfer in a problem domain is to perform a
cognitive task analysis
Transfer
• Near transfer examples
– Racquetball to tennis
– Authentic training session to work setting (e.g.,
airplane simulator)
• Far transfer examples
– Discrete mathematics to programming
• Proof by induction to proving loop correct
– Software development risk management to
retirement investment risk management
– Cooking with a wok to driving
– Riemannian Geometry to General Relativity
Factors Crucial to Strategy Transfer
• User’s conscious evaluation of strategy
effectiveness
• Degree to which learner understands the
conditions under which the strategy applies
• Details of how to apply the strategy
• Attribution of success to effort and strategy
use
• Screening out distracting thoughts
• Amount of relevant declarative knowledge
possessed by strategy user
Transfer
• Intelligence moderates degree of
spontaneous (uncued) transfer and
distance of transfer
• Trainers want trainees to acquire new
knowledge and to use that knowledge at
work
• So, teach work-specific (i.e., domainspecific) knowledge
Bibliography
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Ericsson, K. A., & Charness, N. (1994). Expert performance: Its structure and acquisition.
American Psychologist, 49(8), 725-747.
Ericsson, K. A., Charness, N., Feltovich, P. J., & Hoffman, R. R. (Eds.). (2006). The
Cambridge Handbook of Expertise and Expert Performance. New York: Cambridge
University Press.
Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive
Psychology, 15(1), 1-38.
Gottfredson, L. S. (1996). Societal consequences of the g factor in employment. Journal
of Vocational Behavior, 29(3), 379-410.
Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence,
24(1), 79-132.
Haskell, R. E. (2001). Transfer of learning: Cognition, instruction, and reasoning. San
Diego: Academic Press.
Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.
Johnson-Laird, P. N. (2005). Mental models and thought. In K. J. Holyoak & R. G.
Morrison (eds.), The Cambridge Handbook of thinking and reasoning (pp. 185-208), New
York: Cambridge University Press.
Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci, S. J., Halpern,
D. F., Loehlin, J. C., Perloff, R., Sternberg, R. J., & Urbina, S. (1996). Intelligence:
Knowns and unknowns. American Psychologist, 51(2), 77-101.
Ross, P. E. (2006). The expert mind. Scientific American, 295(2), 64-71.
van Merrienboer, J. G. G. (1997). Training complex cognitive skills: A four-component
instructional design model for technical training. Englewood Cliffs, NJ: Educational
Technology Publications.
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